Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:01, 8.48MB/s]                            
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:08<00:00, 6.91KFile/s]
Downloading celeba: 1.44GB [00:24, 59.0MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
## I had to move this section here so I don't have to pull the data again
data_dir = './data'
import helper
import numpy as np
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f7eb5b0e908>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f7ebc986588>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_input = tf.placeholder(dtype=tf.float32, shape=(None, image_width, image_height, image_channels), name='real_input')
    z_data = tf.placeholder(tf.float32, shape=(None, z_dim), name='z_data')
    lr = tf.placeholder(tf.float32, name='learning_rate')
    
    return real_input, z_data, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.01
    
    def layer(val, filt, shape, strides, normalize=True):
        layer = tf.layers.conv2d(val, filt, shape, strides, padding='same')
        if (normalize == True):
            layer = tf.layers.batch_normalization(layer, training=True)
        
        layer = tf.maximum(alpha * layer, layer)
        
        return layer
    
    with tf.variable_scope('discriminator', reuse=reuse):
        
        hl_1 = layer(images, 128, 5, 2, normalize=False)
        hl_2 = layer(hl_1, 256, 5, 2)
        hl_3 = layer(hl_2, 512, 5, 2)
        
        
        flat = tf.reshape(hl_3, (-1, 4*4*512))
        logits = tf.layers.dense(flat, 1)
        
        # passing the fully connected layer through the sigmoid function to make sure
        # it returns one for real image and zero for fake images
        output = tf.sigmoid(logits)

        return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.01
    
    def layer(val, filt, shape, strides):
        layer = tf.layers.conv2d_transpose(val, filt, shape, strides, padding='SAME')
        layer = tf.layers.batch_normalization(layer, training=is_train)
        layer = tf.maximum(alpha * layer, layer)
        
        return layer
    
    with tf.variable_scope("generator", reuse=not is_train):
        
        h1 = tf.layers.dense(z, 7*7*512)
        h1 = tf.reshape(h1, (-1, 7, 7, 512))
        h1 = tf.layers.batch_normalization(h1, training=is_train)
        h1 = tf.maximum(alpha * h1, h1)
        
        h2 = layer(h1, 256, 5, 1)
        h3 = layer(h2, 128, 5, 2)
    
        logits = tf.layers.conv2d_transpose(h3, out_channel_dim, 5, 2, padding='SAME')
        output = tf.tanh(logits)
        
        
        return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    model = generator(input_z, out_channel_dim)
    
    discriminator_real_model, discriminator_real_logits = discriminator(input_real)
    discriminator_dum_model, discriminator_dum_logits = discriminator(model, True)
    
    # Getting one for real images
    discriminator_r_param = tf.nn.sigmoid_cross_entropy_with_logits(logits=discriminator_real_logits, labels=tf.ones_like(discriminator_real_logits)*np.random.uniform(0.7, 1.2))
    discriminator_real_loss = tf.reduce_mean(discriminator_r_param)
    
    # Getting zero for fake images
    discriminator_d_param = tf.nn.sigmoid_cross_entropy_with_logits(logits=discriminator_dum_logits, labels=tf.zeros_like(discriminator_dum_logits)*np.random.uniform(0.0, 0.3))
    discriminator_dum_loss = tf.reduce_mean(discriminator_d_param)
    
    # The total lost for the discriminator is the sum of lost for the real and the fake images
    discriminator_loss = discriminator_real_loss + discriminator_dum_loss
    
    generator_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=discriminator_dum_logits, labels=tf.ones_like(discriminator_dum_model)))
    
    return discriminator_loss, generator_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed
In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    #get the trainable variables from my Graph
    t_vars = tf.trainable_variables()
    
    # Getting discriminator trainable variables from t_vars
    discriminator_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    
    # Getting generator trainable variables from t_vars
    generator_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=discriminator_vars)
        g_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=generator_vars)
            
        return d_opt, g_opt



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed
In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np


def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
import matplotlib.pyplot as plt
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    steps = 0
    loss_list = []
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                loss_list.append([d_loss.eval({input_z: batch_z, input_real: batch_images}), g_loss.eval({input_z: batch_z})])
                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i + 1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                #print(loss_list)
                if steps % show_every == 0:
                    # reusing the show_n_images variable declared and initialized earlier to create the grid
                    show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)
                    
        plt.plot(loss_list, label='Discriminator')
        plt.plot(loss_list, label='generator')
        plt.ylabel('Loss')
        plt.xlabel('Steps')
        plt.title("Loss Graph")
        plt.show()

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 64
z_dim = 64
learning_rate = 0.001
beta1 = 0.5
print_every = 10
show_every = 100

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.0782... Generator Loss: 3.9786
Epoch 1/2... Discriminator Loss: 0.4180... Generator Loss: 5.4482
Epoch 1/2... Discriminator Loss: 0.2218... Generator Loss: 11.6981
Epoch 1/2... Discriminator Loss: 0.3080... Generator Loss: 3.1211
Epoch 1/2... Discriminator Loss: 0.3882... Generator Loss: 4.5021
Epoch 1/2... Discriminator Loss: 2.0802... Generator Loss: 0.5087
Epoch 1/2... Discriminator Loss: 1.0614... Generator Loss: 1.5386
Epoch 1/2... Discriminator Loss: 0.5252... Generator Loss: 2.3817
Epoch 1/2... Discriminator Loss: 0.8507... Generator Loss: 1.1642
Epoch 1/2... Discriminator Loss: 1.2500... Generator Loss: 0.6888
Epoch 1/2... Discriminator Loss: 1.9028... Generator Loss: 10.6526
Epoch 1/2... Discriminator Loss: 0.3320... Generator Loss: 2.6660
Epoch 1/2... Discriminator Loss: 0.1848... Generator Loss: 4.9595
Epoch 1/2... Discriminator Loss: 4.7601... Generator Loss: 0.0172
Epoch 1/2... Discriminator Loss: 0.2976... Generator Loss: 2.2737
Epoch 1/2... Discriminator Loss: 0.2516... Generator Loss: 2.5160
Epoch 1/2... Discriminator Loss: 0.9807... Generator Loss: 0.9826
Epoch 1/2... Discriminator Loss: 1.4178... Generator Loss: 0.5144
Epoch 1/2... Discriminator Loss: 0.6903... Generator Loss: 3.2996
Epoch 1/2... Discriminator Loss: 0.3133... Generator Loss: 2.3856
Epoch 1/2... Discriminator Loss: 0.2384... Generator Loss: 2.9042
Epoch 1/2... Discriminator Loss: 0.5083... Generator Loss: 1.7031
Epoch 1/2... Discriminator Loss: 2.4889... Generator Loss: 0.1394
Epoch 1/2... Discriminator Loss: 2.2383... Generator Loss: 0.1777
Epoch 1/2... Discriminator Loss: 0.8498... Generator Loss: 0.7885
Epoch 1/2... Discriminator Loss: 1.5812... Generator Loss: 0.5175
Epoch 1/2... Discriminator Loss: 0.5939... Generator Loss: 1.3359
Epoch 1/2... Discriminator Loss: 0.7052... Generator Loss: 1.3541
Epoch 1/2... Discriminator Loss: 0.8076... Generator Loss: 0.9030
Epoch 1/2... Discriminator Loss: 0.5284... Generator Loss: 1.6634
Epoch 1/2... Discriminator Loss: 0.4249... Generator Loss: 2.0127
Epoch 1/2... Discriminator Loss: 0.2336... Generator Loss: 3.1708
Epoch 1/2... Discriminator Loss: 0.3049... Generator Loss: 2.3453
Epoch 1/2... Discriminator Loss: 0.2373... Generator Loss: 3.1739
Epoch 1/2... Discriminator Loss: 0.7616... Generator Loss: 0.9545
Epoch 1/2... Discriminator Loss: 0.9959... Generator Loss: 0.9038
Epoch 1/2... Discriminator Loss: 1.3029... Generator Loss: 0.5433
Epoch 1/2... Discriminator Loss: 2.0303... Generator Loss: 7.6593
Epoch 1/2... Discriminator Loss: 2.2903... Generator Loss: 0.1940
Epoch 1/2... Discriminator Loss: 1.6337... Generator Loss: 0.3225
Epoch 1/2... Discriminator Loss: 1.0282... Generator Loss: 5.5265
Epoch 1/2... Discriminator Loss: 0.9443... Generator Loss: 0.7680
Epoch 1/2... Discriminator Loss: 1.1278... Generator Loss: 0.6386
Epoch 1/2... Discriminator Loss: 0.9094... Generator Loss: 0.9192
Epoch 1/2... Discriminator Loss: 0.3172... Generator Loss: 2.2394
Epoch 1/2... Discriminator Loss: 0.3241... Generator Loss: 3.0898
Epoch 1/2... Discriminator Loss: 2.8535... Generator Loss: 0.1364
Epoch 1/2... Discriminator Loss: 0.7529... Generator Loss: 1.4906
Epoch 1/2... Discriminator Loss: 0.7582... Generator Loss: 1.1854
Epoch 1/2... Discriminator Loss: 0.2754... Generator Loss: 2.7371
Epoch 1/2... Discriminator Loss: 0.3652... Generator Loss: 2.1916
Epoch 1/2... Discriminator Loss: 1.5694... Generator Loss: 0.3335
Epoch 1/2... Discriminator Loss: 0.2289... Generator Loss: 4.1752
Epoch 1/2... Discriminator Loss: 0.2934... Generator Loss: 2.3260
Epoch 1/2... Discriminator Loss: 0.3583... Generator Loss: 2.0919
Epoch 1/2... Discriminator Loss: 0.2384... Generator Loss: 2.9865
Epoch 1/2... Discriminator Loss: 0.3085... Generator Loss: 2.4565
Epoch 1/2... Discriminator Loss: 0.2751... Generator Loss: 3.5969
Epoch 1/2... Discriminator Loss: 0.1845... Generator Loss: 4.3440
Epoch 1/2... Discriminator Loss: 0.3512... Generator Loss: 2.0728
Epoch 1/2... Discriminator Loss: 0.1890... Generator Loss: 5.7113
Epoch 1/2... Discriminator Loss: 0.1684... Generator Loss: 5.8882
Epoch 1/2... Discriminator Loss: 0.2047... Generator Loss: 3.5996
Epoch 1/2... Discriminator Loss: 0.5607... Generator Loss: 3.0247
Epoch 1/2... Discriminator Loss: 0.9086... Generator Loss: 1.1022
Epoch 1/2... Discriminator Loss: 1.0309... Generator Loss: 1.1573
Epoch 1/2... Discriminator Loss: 1.0223... Generator Loss: 0.7519
Epoch 1/2... Discriminator Loss: 1.5418... Generator Loss: 3.7567
Epoch 1/2... Discriminator Loss: 1.9195... Generator Loss: 4.1371
Epoch 1/2... Discriminator Loss: 0.7056... Generator Loss: 1.5661
Epoch 1/2... Discriminator Loss: 1.1365... Generator Loss: 3.4892
Epoch 1/2... Discriminator Loss: 1.0491... Generator Loss: 0.6763
Epoch 1/2... Discriminator Loss: 1.0088... Generator Loss: 0.7974
Epoch 1/2... Discriminator Loss: 1.0108... Generator Loss: 3.5485
Epoch 1/2... Discriminator Loss: 0.6585... Generator Loss: 1.9943
Epoch 1/2... Discriminator Loss: 1.0736... Generator Loss: 1.8984
Epoch 1/2... Discriminator Loss: 0.8937... Generator Loss: 1.4666
Epoch 1/2... Discriminator Loss: 1.2355... Generator Loss: 0.6037
Epoch 1/2... Discriminator Loss: 1.1205... Generator Loss: 4.0684
Epoch 1/2... Discriminator Loss: 0.3940... Generator Loss: 2.2734
Epoch 1/2... Discriminator Loss: 0.4504... Generator Loss: 1.6630
Epoch 1/2... Discriminator Loss: 0.2181... Generator Loss: 4.1119
Epoch 1/2... Discriminator Loss: 0.1765... Generator Loss: 5.0285
Epoch 1/2... Discriminator Loss: 1.6159... Generator Loss: 0.4184
Epoch 1/2... Discriminator Loss: 0.9942... Generator Loss: 0.7421
Epoch 1/2... Discriminator Loss: 0.8622... Generator Loss: 0.8903
Epoch 1/2... Discriminator Loss: 0.5116... Generator Loss: 1.8293
Epoch 1/2... Discriminator Loss: 1.1272... Generator Loss: 0.6198
Epoch 1/2... Discriminator Loss: 0.8088... Generator Loss: 1.2176
Epoch 1/2... Discriminator Loss: 0.5864... Generator Loss: 1.3344
Epoch 1/2... Discriminator Loss: 1.4233... Generator Loss: 0.5182
Epoch 1/2... Discriminator Loss: 0.2691... Generator Loss: 2.8489
Epoch 1/2... Discriminator Loss: 0.2622... Generator Loss: 2.7547
Epoch 2/2... Discriminator Loss: 0.4127... Generator Loss: 1.8946
Epoch 2/2... Discriminator Loss: 1.6439... Generator Loss: 5.6018
Epoch 2/2... Discriminator Loss: 0.5178... Generator Loss: 1.6072
Epoch 2/2... Discriminator Loss: 0.4758... Generator Loss: 1.7533
Epoch 2/2... Discriminator Loss: 0.1743... Generator Loss: 6.9223
Epoch 2/2... Discriminator Loss: 0.2257... Generator Loss: 3.1622
Epoch 2/2... Discriminator Loss: 0.1766... Generator Loss: 5.0951
Epoch 2/2... Discriminator Loss: 0.2130... Generator Loss: 3.7075
Epoch 2/2... Discriminator Loss: 0.1670... Generator Loss: 8.1586
Epoch 2/2... Discriminator Loss: 0.2297... Generator Loss: 3.0810
Epoch 2/2... Discriminator Loss: 0.2955... Generator Loss: 2.5104
Epoch 2/2... Discriminator Loss: 0.4398... Generator Loss: 1.7433
Epoch 2/2... Discriminator Loss: 0.3688... Generator Loss: 2.3544
Epoch 2/2... Discriminator Loss: 0.2722... Generator Loss: 3.5506
Epoch 2/2... Discriminator Loss: 1.2069... Generator Loss: 0.6148
Epoch 2/2... Discriminator Loss: 0.6479... Generator Loss: 2.9475
Epoch 2/2... Discriminator Loss: 0.3555... Generator Loss: 4.1785
Epoch 2/2... Discriminator Loss: 7.9087... Generator Loss: 9.8991
Epoch 2/2... Discriminator Loss: 0.4162... Generator Loss: 1.8493
Epoch 2/2... Discriminator Loss: 0.7235... Generator Loss: 1.1143
Epoch 2/2... Discriminator Loss: 0.5410... Generator Loss: 4.1789
Epoch 2/2... Discriminator Loss: 0.2375... Generator Loss: 3.2401
Epoch 2/2... Discriminator Loss: 0.2008... Generator Loss: 3.5876
Epoch 2/2... Discriminator Loss: 0.5258... Generator Loss: 1.3620
Epoch 2/2... Discriminator Loss: 0.3456... Generator Loss: 2.2472
Epoch 2/2... Discriminator Loss: 0.2904... Generator Loss: 6.2178
Epoch 2/2... Discriminator Loss: 0.2906... Generator Loss: 2.4671
Epoch 2/2... Discriminator Loss: 0.4072... Generator Loss: 1.9413
Epoch 2/2... Discriminator Loss: 0.9497... Generator Loss: 1.1286
Epoch 2/2... Discriminator Loss: 0.6971... Generator Loss: 1.2565
Epoch 2/2... Discriminator Loss: 4.2703... Generator Loss: 6.2737
Epoch 2/2... Discriminator Loss: 1.4668... Generator Loss: 0.4054
Epoch 2/2... Discriminator Loss: 0.6694... Generator Loss: 1.6746
Epoch 2/2... Discriminator Loss: 1.1258... Generator Loss: 0.6556
Epoch 2/2... Discriminator Loss: 0.8320... Generator Loss: 0.8918
Epoch 2/2... Discriminator Loss: 0.6659... Generator Loss: 1.2450
Epoch 2/2... Discriminator Loss: 0.4965... Generator Loss: 1.7550
Epoch 2/2... Discriminator Loss: 1.4829... Generator Loss: 0.6202
Epoch 2/2... Discriminator Loss: 0.3034... Generator Loss: 3.1934
Epoch 2/2... Discriminator Loss: 0.1741... Generator Loss: 5.4192
Epoch 2/2... Discriminator Loss: 0.3319... Generator Loss: 2.2389
Epoch 2/2... Discriminator Loss: 0.2351... Generator Loss: 3.3132
Epoch 2/2... Discriminator Loss: 1.9459... Generator Loss: 0.3579
Epoch 2/2... Discriminator Loss: 1.0427... Generator Loss: 0.7296
Epoch 2/2... Discriminator Loss: 0.5564... Generator Loss: 1.5006
Epoch 2/2... Discriminator Loss: 0.4523... Generator Loss: 1.9567
Epoch 2/2... Discriminator Loss: 0.4750... Generator Loss: 1.6000
Epoch 2/2... Discriminator Loss: 0.4381... Generator Loss: 1.6621
Epoch 2/2... Discriminator Loss: 1.0557... Generator Loss: 0.6968
Epoch 2/2... Discriminator Loss: 2.4211... Generator Loss: 0.1766
Epoch 2/2... Discriminator Loss: 0.9593... Generator Loss: 0.9488
Epoch 2/2... Discriminator Loss: 0.6890... Generator Loss: 1.2998
Epoch 2/2... Discriminator Loss: 0.5328... Generator Loss: 2.4332
Epoch 2/2... Discriminator Loss: 0.7373... Generator Loss: 1.0908
Epoch 2/2... Discriminator Loss: 0.5944... Generator Loss: 1.2473
Epoch 2/2... Discriminator Loss: 0.4822... Generator Loss: 1.6670
Epoch 2/2... Discriminator Loss: 0.3969... Generator Loss: 1.7857
Epoch 2/2... Discriminator Loss: 0.1790... Generator Loss: 4.4896
Epoch 2/2... Discriminator Loss: 0.3525... Generator Loss: 2.1629
Epoch 2/2... Discriminator Loss: 0.3191... Generator Loss: 2.3170
Epoch 2/2... Discriminator Loss: 0.2227... Generator Loss: 3.3858
Epoch 2/2... Discriminator Loss: 0.2487... Generator Loss: 3.0665
Epoch 2/2... Discriminator Loss: 0.1636... Generator Loss: 6.6422
Epoch 2/2... Discriminator Loss: 0.1748... Generator Loss: 5.1130
Epoch 2/2... Discriminator Loss: 0.2090... Generator Loss: 4.5678
Epoch 2/2... Discriminator Loss: 0.1670... Generator Loss: 6.3186
Epoch 2/2... Discriminator Loss: 0.3461... Generator Loss: 2.0964
Epoch 2/2... Discriminator Loss: 0.1849... Generator Loss: 5.5542
Epoch 2/2... Discriminator Loss: 0.2035... Generator Loss: 3.5287
Epoch 2/2... Discriminator Loss: 0.2011... Generator Loss: 3.5244
Epoch 2/2... Discriminator Loss: 0.2456... Generator Loss: 4.5080
Epoch 2/2... Discriminator Loss: 0.4790... Generator Loss: 1.7098
Epoch 2/2... Discriminator Loss: 0.1925... Generator Loss: 3.9082
Epoch 2/2... Discriminator Loss: 2.2525... Generator Loss: 10.2995
Epoch 2/2... Discriminator Loss: 0.6830... Generator Loss: 1.5745
Epoch 2/2... Discriminator Loss: 0.8161... Generator Loss: 1.0006
Epoch 2/2... Discriminator Loss: 0.8085... Generator Loss: 2.2818
Epoch 2/2... Discriminator Loss: 0.9699... Generator Loss: 0.8797
Epoch 2/2... Discriminator Loss: 0.8643... Generator Loss: 0.9263
Epoch 2/2... Discriminator Loss: 1.0128... Generator Loss: 0.7702
Epoch 2/2... Discriminator Loss: 0.6263... Generator Loss: 1.5015
Epoch 2/2... Discriminator Loss: 0.9678... Generator Loss: 3.7860
Epoch 2/2... Discriminator Loss: 1.9129... Generator Loss: 4.3562
Epoch 2/2... Discriminator Loss: 0.9714... Generator Loss: 2.6297
Epoch 2/2... Discriminator Loss: 1.0007... Generator Loss: 0.7682
Epoch 2/2... Discriminator Loss: 1.8150... Generator Loss: 0.3374
Epoch 2/2... Discriminator Loss: 0.7247... Generator Loss: 1.6607
Epoch 2/2... Discriminator Loss: 0.9878... Generator Loss: 0.7546
Epoch 2/2... Discriminator Loss: 2.8757... Generator Loss: 5.4348
Epoch 2/2... Discriminator Loss: 0.8725... Generator Loss: 2.6836
Epoch 2/2... Discriminator Loss: 0.7020... Generator Loss: 1.2107
Epoch 2/2... Discriminator Loss: 1.1560... Generator Loss: 0.6308
Epoch 2/2... Discriminator Loss: 2.9260... Generator Loss: 0.1755
Epoch 2/2... Discriminator Loss: 0.7661... Generator Loss: 2.7850

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 64
z_dim = 64
learning_rate = 0.002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 2.0736... Generator Loss: 0.6459
Epoch 1/1... Discriminator Loss: 1.0503... Generator Loss: 1.2445
Epoch 1/1... Discriminator Loss: 3.0020... Generator Loss: 7.2845
Epoch 1/1... Discriminator Loss: 1.5145... Generator Loss: 2.2549
Epoch 1/1... Discriminator Loss: 0.7935... Generator Loss: 1.7548
Epoch 1/1... Discriminator Loss: 1.9395... Generator Loss: 6.7028
Epoch 1/1... Discriminator Loss: 0.5913... Generator Loss: 3.9408
Epoch 1/1... Discriminator Loss: 0.9229... Generator Loss: 3.0440
Epoch 1/1... Discriminator Loss: 1.3128... Generator Loss: 1.3380
Epoch 1/1... Discriminator Loss: 1.1660... Generator Loss: 3.1757
Epoch 1/1... Discriminator Loss: 2.8552... Generator Loss: 0.1895
Epoch 1/1... Discriminator Loss: 1.1479... Generator Loss: 1.1604
Epoch 1/1... Discriminator Loss: 0.9159... Generator Loss: 1.7264
Epoch 1/1... Discriminator Loss: 1.0061... Generator Loss: 0.9003
Epoch 1/1... Discriminator Loss: 0.7610... Generator Loss: 2.1956
Epoch 1/1... Discriminator Loss: 1.3554... Generator Loss: 0.9827
Epoch 1/1... Discriminator Loss: 0.5934... Generator Loss: 1.9940
Epoch 1/1... Discriminator Loss: 1.4008... Generator Loss: 0.6748
Epoch 1/1... Discriminator Loss: 0.5662... Generator Loss: 1.8936
Epoch 1/1... Discriminator Loss: 0.4705... Generator Loss: 2.9188
Epoch 1/1... Discriminator Loss: 0.5434... Generator Loss: 2.5736
Epoch 1/1... Discriminator Loss: 0.3888... Generator Loss: 5.8033
Epoch 1/1... Discriminator Loss: 0.4386... Generator Loss: 3.6829
Epoch 1/1... Discriminator Loss: 0.8269... Generator Loss: 2.6344
Epoch 1/1... Discriminator Loss: 0.6042... Generator Loss: 2.3062
Epoch 1/1... Discriminator Loss: 0.9049... Generator Loss: 1.1856
Epoch 1/1... Discriminator Loss: 0.5254... Generator Loss: 4.7373
Epoch 1/1... Discriminator Loss: 0.5400... Generator Loss: 3.0811
Epoch 1/1... Discriminator Loss: 0.5787... Generator Loss: 2.6461
Epoch 1/1... Discriminator Loss: 0.4320... Generator Loss: 4.1948
Epoch 1/1... Discriminator Loss: 0.9802... Generator Loss: 2.4491
Epoch 1/1... Discriminator Loss: 0.5720... Generator Loss: 3.7645
Epoch 1/1... Discriminator Loss: 0.4932... Generator Loss: 2.8977
Epoch 1/1... Discriminator Loss: 0.5724... Generator Loss: 2.1010
Epoch 1/1... Discriminator Loss: 1.2674... Generator Loss: 1.5793
Epoch 1/1... Discriminator Loss: 0.6065... Generator Loss: 1.9107
Epoch 1/1... Discriminator Loss: 0.9604... Generator Loss: 1.0260
Epoch 1/1... Discriminator Loss: 0.6646... Generator Loss: 1.8302
Epoch 1/1... Discriminator Loss: 2.3704... Generator Loss: 0.2682
Epoch 1/1... Discriminator Loss: 0.4324... Generator Loss: 3.6477
Epoch 1/1... Discriminator Loss: 0.6028... Generator Loss: 1.9355
Epoch 1/1... Discriminator Loss: 0.5688... Generator Loss: 2.3433
Epoch 1/1... Discriminator Loss: 0.6775... Generator Loss: 1.6277
Epoch 1/1... Discriminator Loss: 0.5793... Generator Loss: 2.8361
Epoch 1/1... Discriminator Loss: 1.4072... Generator Loss: 0.9614
Epoch 1/1... Discriminator Loss: 1.0434... Generator Loss: 1.0132
Epoch 1/1... Discriminator Loss: 0.8499... Generator Loss: 3.0835
Epoch 1/1... Discriminator Loss: 0.6196... Generator Loss: 1.8451
Epoch 1/1... Discriminator Loss: 1.2922... Generator Loss: 2.2529
Epoch 1/1... Discriminator Loss: 0.7561... Generator Loss: 1.5260
Epoch 1/1... Discriminator Loss: 0.5881... Generator Loss: 2.2139
Epoch 1/1... Discriminator Loss: 0.4521... Generator Loss: 9.5132
Epoch 1/1... Discriminator Loss: 1.1837... Generator Loss: 2.0906
Epoch 1/1... Discriminator Loss: 0.4231... Generator Loss: 3.8062
Epoch 1/1... Discriminator Loss: 1.0054... Generator Loss: 0.9421
Epoch 1/1... Discriminator Loss: 1.3697... Generator Loss: 0.6192
Epoch 1/1... Discriminator Loss: 0.9309... Generator Loss: 1.2335
Epoch 1/1... Discriminator Loss: 1.4162... Generator Loss: 0.7236
Epoch 1/1... Discriminator Loss: 1.3528... Generator Loss: 0.5631
Epoch 1/1... Discriminator Loss: 1.3340... Generator Loss: 0.9050
Epoch 1/1... Discriminator Loss: 0.8447... Generator Loss: 1.3428
Epoch 1/1... Discriminator Loss: 0.4742... Generator Loss: 3.8900
Epoch 1/1... Discriminator Loss: 2.2244... Generator Loss: 2.0998
Epoch 1/1... Discriminator Loss: 0.8913... Generator Loss: 1.1867
Epoch 1/1... Discriminator Loss: 1.3529... Generator Loss: 1.7217
Epoch 1/1... Discriminator Loss: 1.1361... Generator Loss: 0.7891
Epoch 1/1... Discriminator Loss: 1.2337... Generator Loss: 0.8191
Epoch 1/1... Discriminator Loss: 0.7192... Generator Loss: 1.3364
Epoch 1/1... Discriminator Loss: 1.0814... Generator Loss: 1.2449
Epoch 1/1... Discriminator Loss: 0.9419... Generator Loss: 1.8857
Epoch 1/1... Discriminator Loss: 0.7609... Generator Loss: 1.4778
Epoch 1/1... Discriminator Loss: 1.1753... Generator Loss: 1.5830
Epoch 1/1... Discriminator Loss: 1.5387... Generator Loss: 0.9189
Epoch 1/1... Discriminator Loss: 0.9596... Generator Loss: 1.1322
Epoch 1/1... Discriminator Loss: 0.7935... Generator Loss: 1.7356
Epoch 1/1... Discriminator Loss: 2.5758... Generator Loss: 0.2827
Epoch 1/1... Discriminator Loss: 0.8780... Generator Loss: 1.1618
Epoch 1/1... Discriminator Loss: 1.3092... Generator Loss: 0.6515
Epoch 1/1... Discriminator Loss: 1.1807... Generator Loss: 1.0911
Epoch 1/1... Discriminator Loss: 0.8015... Generator Loss: 1.2289
Epoch 1/1... Discriminator Loss: 1.0107... Generator Loss: 2.7027
Epoch 1/1... Discriminator Loss: 0.8269... Generator Loss: 1.3562
Epoch 1/1... Discriminator Loss: 1.1340... Generator Loss: 0.9668
Epoch 1/1... Discriminator Loss: 0.7574... Generator Loss: 1.3962
Epoch 1/1... Discriminator Loss: 0.8412... Generator Loss: 1.1162
Epoch 1/1... Discriminator Loss: 0.8196... Generator Loss: 1.1702
Epoch 1/1... Discriminator Loss: 1.0191... Generator Loss: 1.0270
Epoch 1/1... Discriminator Loss: 0.7592... Generator Loss: 5.1668
Epoch 1/1... Discriminator Loss: 1.1024... Generator Loss: 0.9906
Epoch 1/1... Discriminator Loss: 1.2169... Generator Loss: 0.7435
Epoch 1/1... Discriminator Loss: 0.8434... Generator Loss: 1.1559
Epoch 1/1... Discriminator Loss: 0.6413... Generator Loss: 1.8406
Epoch 1/1... Discriminator Loss: 0.9216... Generator Loss: 1.1744
Epoch 1/1... Discriminator Loss: 0.7691... Generator Loss: 1.5882
Epoch 1/1... Discriminator Loss: 0.8409... Generator Loss: 1.3687
Epoch 1/1... Discriminator Loss: 1.5257... Generator Loss: 2.7657
Epoch 1/1... Discriminator Loss: 1.2330... Generator Loss: 1.7925
Epoch 1/1... Discriminator Loss: 1.2760... Generator Loss: 2.5904
Epoch 1/1... Discriminator Loss: 0.9275... Generator Loss: 1.4784
Epoch 1/1... Discriminator Loss: 1.6783... Generator Loss: 0.5012
Epoch 1/1... Discriminator Loss: 0.8775... Generator Loss: 1.4858
Epoch 1/1... Discriminator Loss: 0.6563... Generator Loss: 1.7606
Epoch 1/1... Discriminator Loss: 0.8192... Generator Loss: 1.7379
Epoch 1/1... Discriminator Loss: 0.9794... Generator Loss: 1.0512
Epoch 1/1... Discriminator Loss: 1.0095... Generator Loss: 1.5839
Epoch 1/1... Discriminator Loss: 1.0526... Generator Loss: 0.8791
Epoch 1/1... Discriminator Loss: 0.9928... Generator Loss: 1.0865
Epoch 1/1... Discriminator Loss: 1.1964... Generator Loss: 1.2338
Epoch 1/1... Discriminator Loss: 1.0049... Generator Loss: 0.9135
Epoch 1/1... Discriminator Loss: 1.3972... Generator Loss: 0.6633
Epoch 1/1... Discriminator Loss: 0.8214... Generator Loss: 1.1989
Epoch 1/1... Discriminator Loss: 1.1580... Generator Loss: 0.8312
Epoch 1/1... Discriminator Loss: 0.6160... Generator Loss: 2.0956
Epoch 1/1... Discriminator Loss: 1.0893... Generator Loss: 2.2330
Epoch 1/1... Discriminator Loss: 1.0971... Generator Loss: 2.6866
Epoch 1/1... Discriminator Loss: 1.0291... Generator Loss: 0.9741
Epoch 1/1... Discriminator Loss: 0.9709... Generator Loss: 1.8368
Epoch 1/1... Discriminator Loss: 1.3135... Generator Loss: 0.7445
Epoch 1/1... Discriminator Loss: 0.8783... Generator Loss: 1.3599
Epoch 1/1... Discriminator Loss: 0.8763... Generator Loss: 2.1802
Epoch 1/1... Discriminator Loss: 1.7448... Generator Loss: 0.6480
Epoch 1/1... Discriminator Loss: 1.1076... Generator Loss: 0.8674
Epoch 1/1... Discriminator Loss: 0.6607... Generator Loss: 2.3789
Epoch 1/1... Discriminator Loss: 0.6390... Generator Loss: 1.7808
Epoch 1/1... Discriminator Loss: 1.0517... Generator Loss: 0.8668
Epoch 1/1... Discriminator Loss: 0.9625... Generator Loss: 1.4385
Epoch 1/1... Discriminator Loss: 0.4447... Generator Loss: 3.1106
Epoch 1/1... Discriminator Loss: 0.8132... Generator Loss: 1.5359
Epoch 1/1... Discriminator Loss: 0.6969... Generator Loss: 3.2923
Epoch 1/1... Discriminator Loss: 0.4260... Generator Loss: 3.2735
Epoch 1/1... Discriminator Loss: 0.8384... Generator Loss: 1.8175
Epoch 1/1... Discriminator Loss: 0.7542... Generator Loss: 2.2752
Epoch 1/1... Discriminator Loss: 1.0335... Generator Loss: 0.8823
Epoch 1/1... Discriminator Loss: 0.7076... Generator Loss: 1.5923
Epoch 1/1... Discriminator Loss: 0.5770... Generator Loss: 2.0694
Epoch 1/1... Discriminator Loss: 0.5711... Generator Loss: 1.9108
Epoch 1/1... Discriminator Loss: 0.6458... Generator Loss: 1.5908
Epoch 1/1... Discriminator Loss: 0.9998... Generator Loss: 1.1084
Epoch 1/1... Discriminator Loss: 0.8600... Generator Loss: 1.2075
Epoch 1/1... Discriminator Loss: 1.1840... Generator Loss: 1.7248
Epoch 1/1... Discriminator Loss: 0.9197... Generator Loss: 1.7724
Epoch 1/1... Discriminator Loss: 0.9510... Generator Loss: 1.2262
Epoch 1/1... Discriminator Loss: 0.7854... Generator Loss: 1.9628
Epoch 1/1... Discriminator Loss: 1.1164... Generator Loss: 1.2952
Epoch 1/1... Discriminator Loss: 0.8133... Generator Loss: 1.5447
Epoch 1/1... Discriminator Loss: 1.0327... Generator Loss: 1.9421
Epoch 1/1... Discriminator Loss: 0.8060... Generator Loss: 1.3716
Epoch 1/1... Discriminator Loss: 0.9270... Generator Loss: 1.2560
Epoch 1/1... Discriminator Loss: 1.0594... Generator Loss: 1.0179
Epoch 1/1... Discriminator Loss: 0.9398... Generator Loss: 1.8605
Epoch 1/1... Discriminator Loss: 0.9204... Generator Loss: 1.0536
Epoch 1/1... Discriminator Loss: 0.9787... Generator Loss: 1.6990
Epoch 1/1... Discriminator Loss: 1.0555... Generator Loss: 1.0660
Epoch 1/1... Discriminator Loss: 1.9603... Generator Loss: 3.6369
Epoch 1/1... Discriminator Loss: 0.7577... Generator Loss: 1.3906
Epoch 1/1... Discriminator Loss: 1.2054... Generator Loss: 1.0036
Epoch 1/1... Discriminator Loss: 0.9023... Generator Loss: 2.1361
Epoch 1/1... Discriminator Loss: 1.2862... Generator Loss: 0.9400
Epoch 1/1... Discriminator Loss: 1.0443... Generator Loss: 1.0137
Epoch 1/1... Discriminator Loss: 1.2573... Generator Loss: 1.0307
Epoch 1/1... Discriminator Loss: 1.0761... Generator Loss: 1.0728
Epoch 1/1... Discriminator Loss: 0.9261... Generator Loss: 1.5853
Epoch 1/1... Discriminator Loss: 0.7400... Generator Loss: 1.7336
Epoch 1/1... Discriminator Loss: 1.2664... Generator Loss: 0.9316
Epoch 1/1... Discriminator Loss: 1.1196... Generator Loss: 1.2009
Epoch 1/1... Discriminator Loss: 0.8118... Generator Loss: 1.2623
Epoch 1/1... Discriminator Loss: 1.4334... Generator Loss: 0.7336
Epoch 1/1... Discriminator Loss: 0.8435... Generator Loss: 1.8542
Epoch 1/1... Discriminator Loss: 1.0549... Generator Loss: 0.8750
Epoch 1/1... Discriminator Loss: 1.1334... Generator Loss: 2.0494
Epoch 1/1... Discriminator Loss: 0.8702... Generator Loss: 1.2998
Epoch 1/1... Discriminator Loss: 1.0636... Generator Loss: 1.1054
Epoch 1/1... Discriminator Loss: 1.0559... Generator Loss: 1.0799
Epoch 1/1... Discriminator Loss: 1.0063... Generator Loss: 1.6386
Epoch 1/1... Discriminator Loss: 0.8802... Generator Loss: 1.4491
Epoch 1/1... Discriminator Loss: 0.8369... Generator Loss: 1.1514
Epoch 1/1... Discriminator Loss: 1.0291... Generator Loss: 1.1474
Epoch 1/1... Discriminator Loss: 0.8754... Generator Loss: 1.4482
Epoch 1/1... Discriminator Loss: 0.8666... Generator Loss: 1.4303
Epoch 1/1... Discriminator Loss: 1.1447... Generator Loss: 1.1010
Epoch 1/1... Discriminator Loss: 0.9622... Generator Loss: 1.2103
Epoch 1/1... Discriminator Loss: 0.8322... Generator Loss: 1.3690
Epoch 1/1... Discriminator Loss: 1.0346... Generator Loss: 1.3755
Epoch 1/1... Discriminator Loss: 0.9972... Generator Loss: 1.0778
Epoch 1/1... Discriminator Loss: 0.9064... Generator Loss: 1.2934
Epoch 1/1... Discriminator Loss: 1.3199... Generator Loss: 0.6703
Epoch 1/1... Discriminator Loss: 1.0003... Generator Loss: 1.0250
Epoch 1/1... Discriminator Loss: 0.8562... Generator Loss: 1.2370
Epoch 1/1... Discriminator Loss: 0.8401... Generator Loss: 1.2385
Epoch 1/1... Discriminator Loss: 1.0211... Generator Loss: 1.0127
Epoch 1/1... Discriminator Loss: 1.2104... Generator Loss: 1.0580
Epoch 1/1... Discriminator Loss: 0.9602... Generator Loss: 1.3835
Epoch 1/1... Discriminator Loss: 0.7659... Generator Loss: 1.9209
Epoch 1/1... Discriminator Loss: 0.9373... Generator Loss: 0.9788
Epoch 1/1... Discriminator Loss: 1.3133... Generator Loss: 1.0023
Epoch 1/1... Discriminator Loss: 1.0244... Generator Loss: 1.0090
Epoch 1/1... Discriminator Loss: 0.7495... Generator Loss: 1.4177
Epoch 1/1... Discriminator Loss: 0.7019... Generator Loss: 1.4070
Epoch 1/1... Discriminator Loss: 0.8257... Generator Loss: 1.1974
Epoch 1/1... Discriminator Loss: 0.8085... Generator Loss: 1.2152
Epoch 1/1... Discriminator Loss: 1.1013... Generator Loss: 0.9013
Epoch 1/1... Discriminator Loss: 0.8603... Generator Loss: 1.2683
Epoch 1/1... Discriminator Loss: 1.1120... Generator Loss: 1.0022
Epoch 1/1... Discriminator Loss: 1.0377... Generator Loss: 1.8538
Epoch 1/1... Discriminator Loss: 0.9761... Generator Loss: 1.3315
Epoch 1/1... Discriminator Loss: 0.8872... Generator Loss: 1.8480
Epoch 1/1... Discriminator Loss: 1.0501... Generator Loss: 1.2462
Epoch 1/1... Discriminator Loss: 0.9154... Generator Loss: 1.0329
Epoch 1/1... Discriminator Loss: 1.0188... Generator Loss: 1.2432
Epoch 1/1... Discriminator Loss: 0.8120... Generator Loss: 1.3168
Epoch 1/1... Discriminator Loss: 1.0345... Generator Loss: 1.7441
Epoch 1/1... Discriminator Loss: 0.7825... Generator Loss: 1.5163
Epoch 1/1... Discriminator Loss: 1.1343... Generator Loss: 0.8600
Epoch 1/1... Discriminator Loss: 0.9691... Generator Loss: 1.4901
Epoch 1/1... Discriminator Loss: 1.1763... Generator Loss: 0.9107
Epoch 1/1... Discriminator Loss: 0.9625... Generator Loss: 1.0661
Epoch 1/1... Discriminator Loss: 0.8502... Generator Loss: 1.1648
Epoch 1/1... Discriminator Loss: 0.9792... Generator Loss: 0.9398
Epoch 1/1... Discriminator Loss: 1.0593... Generator Loss: 1.5287
Epoch 1/1... Discriminator Loss: 1.0051... Generator Loss: 1.4449
Epoch 1/1... Discriminator Loss: 0.8573... Generator Loss: 1.1329
Epoch 1/1... Discriminator Loss: 0.7706... Generator Loss: 1.2528
Epoch 1/1... Discriminator Loss: 1.0880... Generator Loss: 0.8644
Epoch 1/1... Discriminator Loss: 1.1037... Generator Loss: 1.3241
Epoch 1/1... Discriminator Loss: 0.9028... Generator Loss: 1.6877
Epoch 1/1... Discriminator Loss: 1.1354... Generator Loss: 0.8601
Epoch 1/1... Discriminator Loss: 1.0893... Generator Loss: 1.2330
Epoch 1/1... Discriminator Loss: 0.8639... Generator Loss: 1.3206
Epoch 1/1... Discriminator Loss: 2.0360... Generator Loss: 2.6791
Epoch 1/1... Discriminator Loss: 1.1441... Generator Loss: 1.0789
Epoch 1/1... Discriminator Loss: 1.0356... Generator Loss: 1.0914
Epoch 1/1... Discriminator Loss: 1.7271... Generator Loss: 0.3905
Epoch 1/1... Discriminator Loss: 1.3063... Generator Loss: 0.9500
Epoch 1/1... Discriminator Loss: 1.4099... Generator Loss: 0.6129
Epoch 1/1... Discriminator Loss: 1.3920... Generator Loss: 1.0041
Epoch 1/1... Discriminator Loss: 1.3414... Generator Loss: 1.0238
Epoch 1/1... Discriminator Loss: 1.3174... Generator Loss: 0.7726
Epoch 1/1... Discriminator Loss: 1.3275... Generator Loss: 1.0150
Epoch 1/1... Discriminator Loss: 1.3450... Generator Loss: 0.8585
Epoch 1/1... Discriminator Loss: 1.3240... Generator Loss: 0.7951
Epoch 1/1... Discriminator Loss: 1.3169... Generator Loss: 1.0511
Epoch 1/1... Discriminator Loss: 1.4793... Generator Loss: 1.2440
Epoch 1/1... Discriminator Loss: 1.2295... Generator Loss: 1.0330
Epoch 1/1... Discriminator Loss: 1.3300... Generator Loss: 0.9445
Epoch 1/1... Discriminator Loss: 1.2477... Generator Loss: 0.9112
Epoch 1/1... Discriminator Loss: 1.3144... Generator Loss: 0.7303
Epoch 1/1... Discriminator Loss: 1.3537... Generator Loss: 1.0116
Epoch 1/1... Discriminator Loss: 1.2919... Generator Loss: 1.0372
Epoch 1/1... Discriminator Loss: 1.2162... Generator Loss: 1.1340
Epoch 1/1... Discriminator Loss: 1.3440... Generator Loss: 0.6886
Epoch 1/1... Discriminator Loss: 1.3229... Generator Loss: 0.8481
Epoch 1/1... Discriminator Loss: 1.2740... Generator Loss: 0.8555
Epoch 1/1... Discriminator Loss: 1.2773... Generator Loss: 1.1073
Epoch 1/1... Discriminator Loss: 1.3324... Generator Loss: 1.0221
Epoch 1/1... Discriminator Loss: 1.3342... Generator Loss: 0.6509
Epoch 1/1... Discriminator Loss: 1.2519... Generator Loss: 0.8396
Epoch 1/1... Discriminator Loss: 1.2876... Generator Loss: 0.8211
Epoch 1/1... Discriminator Loss: 1.2401... Generator Loss: 0.9830
Epoch 1/1... Discriminator Loss: 1.3180... Generator Loss: 0.7189
Epoch 1/1... Discriminator Loss: 1.2617... Generator Loss: 0.9825
Epoch 1/1... Discriminator Loss: 1.2505... Generator Loss: 0.6560
Epoch 1/1... Discriminator Loss: 1.3379... Generator Loss: 1.1251
Epoch 1/1... Discriminator Loss: 1.2276... Generator Loss: 0.9970
Epoch 1/1... Discriminator Loss: 1.2561... Generator Loss: 1.0473
Epoch 1/1... Discriminator Loss: 1.3178... Generator Loss: 0.8786
Epoch 1/1... Discriminator Loss: 1.1547... Generator Loss: 0.9522
Epoch 1/1... Discriminator Loss: 1.2019... Generator Loss: 1.0255
Epoch 1/1... Discriminator Loss: 1.3738... Generator Loss: 0.9948
Epoch 1/1... Discriminator Loss: 1.2172... Generator Loss: 1.1175
Epoch 1/1... Discriminator Loss: 1.1776... Generator Loss: 1.1688
Epoch 1/1... Discriminator Loss: 1.2922... Generator Loss: 0.9710
Epoch 1/1... Discriminator Loss: 1.1234... Generator Loss: 0.8831
Epoch 1/1... Discriminator Loss: 1.1676... Generator Loss: 0.7647
Epoch 1/1... Discriminator Loss: 1.2485... Generator Loss: 1.1764
Epoch 1/1... Discriminator Loss: 1.1286... Generator Loss: 1.1209
Epoch 1/1... Discriminator Loss: 1.1703... Generator Loss: 1.1267
Epoch 1/1... Discriminator Loss: 1.1798... Generator Loss: 0.9669
Epoch 1/1... Discriminator Loss: 1.1124... Generator Loss: 0.9977
Epoch 1/1... Discriminator Loss: 1.5344... Generator Loss: 1.7674
Epoch 1/1... Discriminator Loss: 1.1619... Generator Loss: 1.0330
Epoch 1/1... Discriminator Loss: 1.1436... Generator Loss: 1.2055
Epoch 1/1... Discriminator Loss: 1.1869... Generator Loss: 1.0637
Epoch 1/1... Discriminator Loss: 1.1566... Generator Loss: 0.8697
Epoch 1/1... Discriminator Loss: 1.2115... Generator Loss: 1.2795
Epoch 1/1... Discriminator Loss: 1.1524... Generator Loss: 1.1620
Epoch 1/1... Discriminator Loss: 1.0159... Generator Loss: 1.1679
Epoch 1/1... Discriminator Loss: 1.1125... Generator Loss: 0.7424
Epoch 1/1... Discriminator Loss: 1.3017... Generator Loss: 0.8737
Epoch 1/1... Discriminator Loss: 1.5637... Generator Loss: 0.7362
Epoch 1/1... Discriminator Loss: 1.0040... Generator Loss: 1.0488
Epoch 1/1... Discriminator Loss: 1.1684... Generator Loss: 1.0505
Epoch 1/1... Discriminator Loss: 0.9793... Generator Loss: 1.1838
Epoch 1/1... Discriminator Loss: 0.8946... Generator Loss: 1.0717
Epoch 1/1... Discriminator Loss: 1.2719... Generator Loss: 2.0549
Epoch 1/1... Discriminator Loss: 0.7740... Generator Loss: 1.1636
Epoch 1/1... Discriminator Loss: 0.8917... Generator Loss: 1.1304
Epoch 1/1... Discriminator Loss: 0.9951... Generator Loss: 1.6880
Epoch 1/1... Discriminator Loss: 0.9521... Generator Loss: 0.9043
Epoch 1/1... Discriminator Loss: 1.5307... Generator Loss: 0.4384
Epoch 1/1... Discriminator Loss: 0.8853... Generator Loss: 1.0628
Epoch 1/1... Discriminator Loss: 0.9491... Generator Loss: 1.2070
Epoch 1/1... Discriminator Loss: 1.0890... Generator Loss: 1.3211
Epoch 1/1... Discriminator Loss: 0.7612... Generator Loss: 1.3206
Epoch 1/1... Discriminator Loss: 1.0383... Generator Loss: 1.0728
Epoch 1/1... Discriminator Loss: 0.6601... Generator Loss: 1.5829
Epoch 1/1... Discriminator Loss: 1.4803... Generator Loss: 1.6555
Epoch 1/1... Discriminator Loss: 1.0344... Generator Loss: 0.8774
Epoch 1/1... Discriminator Loss: 0.9726... Generator Loss: 0.9997
Epoch 1/1... Discriminator Loss: 0.9385... Generator Loss: 1.0347
Epoch 1/1... Discriminator Loss: 1.1086... Generator Loss: 1.0321
Epoch 1/1... Discriminator Loss: 1.1463... Generator Loss: 0.8170
Epoch 1/1... Discriminator Loss: 1.1053... Generator Loss: 0.7465
Epoch 1/1... Discriminator Loss: 1.5687... Generator Loss: 0.5308
Epoch 1/1... Discriminator Loss: 0.9382... Generator Loss: 1.1430
Epoch 1/1... Discriminator Loss: 1.0031... Generator Loss: 0.8817
Epoch 1/1... Discriminator Loss: 1.1805... Generator Loss: 1.3484

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.